Overview
Nate B. Jones builds on his original Open Brain concept — a personal Supabase database connected to AI tools via MCP — by showing how to add visual interfaces ("the human door") and practical extensions that make the system genuinely useful in daily life.
Key Takeaways
- The keyhole problem: Chatting with AI through a text interface is powerful but limited. You need both an agent-readable interface (MCP) and a human-readable interface (visual app) pointing at the same data.
- One source of truth, two doors: The database table is the shared surface. Your agent reads/writes via MCP; you read/write via a simple web app. No sync layer, no export, no middleware.
- Build the visual layer with AI: Describe what you want to your AI (Claude, ChatGPT), get a web app generated, then deploy it for free on Vercel. Bookmark it on your phone and it behaves like a native app.
- The AI flywheel: Every time models improve, your entire Open Brain system automatically gets smarter. You are just logging data and building patterns; the intelligence compounds over time.
Core Architecture
The Two-Door Pattern
- Agent door: MCP server connected to your Supabase database — the agent queries, writes, reasons across tables
- Human door: A visual web app (deployed on Vercel) that reads from and writes to the same tables
- Both sides have direct access to the same rows — consistency is architectural, not managed by a sync layer
How to Build a Visual Extension
- Create the table and columns in your Supabase Open Brain instance
- Describe the visual you want to your AI (e.g., "mobile-friendly maintenance tracker with warranty dates highlighted")
- Iterate on the generated app with the AI
- Deploy to Vercel (free tier) to get a live URL
- Bookmark on your phone — it behaves like an app without the App Store
Use Cases
Household Knowledge Base
Capture scattered household info (paint colors, plumber contacts, Wi-Fi passwords, kids' shoe sizes) into a structured table during normal AI conversations. Build a searchable, categorized visual interface over it.
Professional Relationship Tracker
Log interactions with professional contacts. Ask your AI "anyone I've been neglecting?" and it scans for gaps. The visual shows at-a-glance which relationships need attention, with the ability to filter by topic or industry.
Job Search Dashboard
Track companies, roles, contacts, interviews, follow-ups, and resume versions across tables. Cross-category reasoning surfaces warm introductions from conference notes and relationship data. An autonomous agent can catch expiring follow-up windows you forgot about.
Three Principles for Choosing Problems
- Time bridging: Agent memory doesn't decay like human memory. Any problem where value comes from linking events across months or years is agent territory (maintenance schedules, recurring compliance tasks).
- Cross-category reasoning: The power is in connections between tables that humans don't naturally cross-reference (e.g., dishwasher maintenance status + meal planning).
- Agent surfaces, human decides, agent executes: The agent handles memory and pattern recognition; you handle judgment and decisions. This division keeps the system trustworthy.
Key Concepts
- Open Brain: A personal Supabase database with an MCP server that any AI client can connect to
- MCP (Model Context Protocol): The protocol that lets AI agents read/write to your database directly
- Vercel: Free hosting service for deploying the visual web apps
- Supabase: Open-source database platform where the Open Brain data lives
- Agent-readable surface: Data structured so AI can query and reason about it, not just scrape a UI
Practical Advice
- You don't need an autonomous agent (like Open Claw) to benefit — Claude or ChatGPT with MCP access already provides huge value
- Start with Supabase's built-in table editor before building custom visuals
- You can use Lovable as a shortcut for the visual layer, or build it yourself with AI-generated code for free
- Once you deploy your first Vercel app, adding more is trivially easy
- The pattern is the same regardless of domain: define the table structure, point your agent at it through MCP, point yourself at it through a visual interface